Plano, Texas · Senior Business Analyst · Salesforce · AWS · AI
Senior Business Analyst at HotelKey, working at the scope of a solution architect. Five-plus years across hospitality SaaS, automotive, and financial services, sitting where enterprise clients, product, and engineering meet: running discovery, designing the Salesforce, AWS, and API integrations that follow, testing them to certification, and increasingly wiring GenAI into the delivery itself.
Most of my work happens in the gap between what a hotel brand's operations team needs and what an engineering team can ship. I run the discovery sessions, write the user stories and data-mapping rules, build the Postman collections that prove a CRS integration works, and walk clients through UAT and partner certification until the feature matches the logic we agreed on. Then I do it again for the next brand. The title says analyst; the day-to-day is the solution design, the integration decisions, and owning that they hold up in production.
On the platform side I'm a Salesforce specialist (Apex, Lightning Web Components, Flows, Einstein) with five certifications and a preference for declarative solutions that survive the next admin. On the cloud side I work in AWS daily: reservation data products on Kinesis, Glue, and Athena, and API performance against DynamoDB-backed services. On the data side I write the SQL and Athena queries that audit what the APIs actually did.
Lately the interesting question is what agentic AI looks like inside a governed enterprise platform. My answer-in-progress is Trowel, an AI agent that reads a Salesforce org's metadata and produces the kind of Well-Architected assessment consultancies bill five figures for. The other eleven projects below are the same instinct pointed at APIs, delivery, and hospitality data.
Week 2 of an 8-week build of Trowel for Salesforce · studying for Salesforce AI Specialist · shipping one project a week through September.
Nine-plus brands on a single org: brand as a data dimension, declarative-first automation, Einstein where it changed a decision.
+20% conversion · 70%+ FCR→ 02 · APIs & integrationSpec-as-contract, Postman collections, written pass/fail certification, SQL audits, and SLA tracking across 10+ mission-critical APIs.
30% fewer API support tickets→ 03 · AWS & dataKinesis, Glue, and Athena for live booking visibility, plus validating a DynamoDB DAX rollout against the SLAs brands actually enforce.
Validated · signed off→A public build log, not a trophy case. Each entry ships to GitHub when it meets its own definition of done; repos flip from coming soon to live as they land. Click a row for scope, stack, and the impact it's designed to have.
Excavates an org's metadata (Flows, Apex, fields, permission sets) into a dependency graph, then an agent with hybrid retrieval (graph traversal + embeddings) finds dead automation, conflicting triggers, and permission sprawl and narrates a prioritized remediation roadmap. Findings are detected deterministically; the LLM explains, never invents.
Replaces a $50k+ manual org-health assessment with a reproducible report in minutes, gated by a golden-dataset eval suite in CI.
Parses .flow-meta.xml and flags the things that hurt in production: missing fault paths, hardcoded record IDs, DML inside loops, recursion risk from record-triggered Flows updating their own object, and inactive-but-referenced Flows. Runs locally or as a CI check on every pull request.
Catches the top five Flow defects before deployment instead of after a support ticket, the same class of issue that drove 30% of API support volume at scale.
Codifies a CRS partner-certification cycle as a versioned Postman collection with data-driven test cases, runs it against sandbox/staging via Newman, and generates the pass/fail certification document reviewers actually want, with payload diffs for every failure.
Turns a multi-day manual certification into a repeatable one-command run with an audit-ready report.
Probes a set of REST endpoints on a schedule, stores response time and status, computes rolling uptime and p95 latency against per-integration SLA thresholds, and posts breach alerts to Slack or email. A small dashboard shows the last 30 days per partner.
Makes SLA tracking a system instead of a spreadsheet, so escalations happen on data, not on the client noticing first.
A BA writes a story in plain English; the tool generates a record-triggered Flow definition, a Gherkin acceptance-test file, and a human-review diff before anything is deployed. Schema-forced output keeps the metadata valid; a review step keeps the human in the loop.
Compresses the story-to-working-automation loop from days to an afternoon while producing the test artifacts UAT needs anyway.
Ingests meeting transcripts and ticket updates, extracts RAID items with owners and dates, de-duplicates against the existing log, and flags risks that resurface across sprints. Produces the weekly status draft a program manager would otherwise write by hand.
Designed to cut program-review prep time by a further 20% on top of what GenAI reporting already delivered at HotelKey.
Embeds and clusters support tickets across properties and brands, separates "one property's configuration error" from "platform-wide defect," and drafts the RCA document with the affected-property list attached. Built on synthetic hospitality ticket data.
Surfaces platform defects days earlier by seeing the pattern across 1,000 properties that no single support agent can.
Versions Prompt Builder templates alongside a golden dataset of inputs and expected outputs, scores each template version with a rubric-driven judge model, and reports regressions before a template is promoted. The eval discipline from Trowel, pointed at Einstein.
Gives admins a way to change a production prompt with evidence instead of vibes, which is the missing piece in most Agentforce rollouts.
Takes two system schemas (say, a CRS reservation payload and a CRM object) and a mapping definition, then emits the data-mapping document for stakeholders and the SQL queries that verify the mapped data landed correctly. One source of truth for the BA doc and the audit.
Ends the drift between the mapping spec everyone signed and the data that actually arrives.
Accepts leads from any source, applies configurable routing rules (territory, capacity, round-robin, score thresholds), writes assignments to Salesforce through the REST API with OAuth, and exposes a webhook so downstream systems learn about the assignment in real time. Postman collection and OpenAPI spec included.
Demonstrates the full API product lifecycle (design, auth, docs, tests, versioning) on a workflow every sales org has.
Packages the forecasting pattern from my CG Infinity work as a template: dbt models with data-quality tests, a Python forecasting layer, and a Power BI report wired to Snowflake, with a seed dataset so anyone can run it end to end.
The version of "35% less manual analysis time" that someone else can clone and stand up in an afternoon.
Reads a hotel brand's integration requirements and produces the onboarding playbook: property-level checklist, configuration steps, UAT scripts with acceptance criteria, and a go-live sign-off sheet, delivered as a formatted Word document the client can actually use.
Standardizes the onboarding artifacts behind the 30% implementation-effort reduction so every new brand starts from the same playbook.
Apex, Lightning Web Components, Flows, Einstein AI, Data Cloud. Well-Architected by habit: declarative first, code where it earns its place, CI/CD around all of it.
Kinesis, Glue, Athena, DynamoDB, S3, Lambda, API Gateway. Reservation data pipelines and near-real-time booking visibility; validating API performance and SLAs on DynamoDB-backed services.
Postman collections, certification cycles, SLA monitoring, payload auditing with SQL. I treat an integration as untested until the client's data has landed and been queried.
Claude, Einstein, prompt engineering, hybrid retrieval, LLM-as-judge evals. The interesting part isn't the model; it's the eval gate that makes it safe to ship.
Discovery, user stories, acceptance criteria, data mapping, UAT, partner certification, backlog prioritization, Agile/Scrum, Jira & Confluence. Client-facing by default; every requirement traceable to a test and a metric.
SQL, Snowflake, dbt, Power BI, Tableau, Python, Alteryx. Pipelines with quality checks, dashboards leadership actually opens.
Verify: Salesforce Trailblazer profile · AWS on Credly
I read everything that comes in. Fastest replies go to specific questions: a role, a project, an org that needs an honest look.